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A Lightweight Data Interchange Format for Internet of Things in the Palcom Middleware Framework

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A Lightweight Data Interchange Format for Internet of Things in the Palcom Middleware Framework Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 56 ( 2015 ) 284 – 291 The 10th International Conference on Future Networks and Communications (FNC 2015) A lightweight data interchange format for Internet of Things in the PalCom middleware framework. Mattias Nordahla,*, Boris Magnussona aLund University, Computer Science, Lund, Sweden Abstract We present the PalCom Object Notation, a textual data representation format for communication between internet of things which support binary and textual data. The format does not require parsing of user data (or the “payload”) and is thus efficient to use also for large binary values such as digital images, audio and video as well as for short textual values. These can be mixed in the same messages and thus transported over the same communication link. Its structure is influenced by JSON, making it easy to translate between the two formats. The evaluation show that its size and processing efficiency is comparable to that of JSON for small data, but becomes both smaller and more efficient as data grows, and can yield a tenfold performance increase for binary payloads. ©© 20120155 TheThe Authors.Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PeerPeer-review-review under under responsibility responsibility of ofthe the Conference Conference Program Program Chairs Chairs. Keywords: Lightweight protocol; Data interchange format; PON; JSON; Textual format; Efficient binary handling; PalCom; Java 1. Introduction In this paper we introduce the PalCom Object Notation (PON), a lightweight format for data interchange developed for use in PalCom. PalCom is a pervasive middleware framework implemented in Java that aims to simplify the creation of dynamic networks between devices in distributed systems, and to give an easy and intuitive * Corresponding author. Tel.: +46-46-2220000 E-mail address: [email protected] 1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs doi: 10.1016/j.procs.2015.07.210 Mattias Nordahl and Boris Magnusson / Procedia Computer Science 56 ( 2015 ) 284 – 291 285 way for combining services provided by the devices.1 It allows users to combine services from any device on the network into larger units with more complex functionality. PalCom has automatic device and service discovery, as described by a discovery protocol, which entails data of various types being sent between devices. Communication between services themselves are governed by a service-interaction protocol, but what information are sent with it are up to the service implementer. There is thus a need to be able to send a mixture of types of information (text, structured information, binary information such as images, audio and video) between devices in a general and homogeneous way. This is exemplified well in the itACiH project, which develops, on top of PalCom, a system to support advanced care in the home with support for visiting nurses, on-line equipment in the patients’ home, and process support at hospital wards. Previously all PalCom protocols has been represented in XML3, with only limited type support for service communications, as everything was represented as strings. One part of the itACiH project is a web-server with PalCom as backend8, in which JSON2 has been used for communication with clients, which gives direct support for different data types. But as the project progressed a requirement arose for being able to send binary information as well. However, JSON, just as XML, lack the direct ability to represent such data, and must resort to special encodings which increase their size and adds extra processing. This motivated the development of a new compact, textual format, similar to JSON, but which could directly handle binary information. In order to outline the design space we take examples from early language design which also illustrate that these problems are nothing new. 1.1. String handling in FORTRAN Handling of strings in Standard FORTRAN4, Hollerith constants, was an important influence in designing the PalCom Object Notation. Its format statement was extended to include strings: FORMAT(13H HELLO, WORLD) In this format the programmer was to count the number of characters in the string and give the compiler the length before the ‘H’ which indicated the string type. Although the manual counting could be a problem, this format had the advantages that 1) the FORTRAN complier did not need to parse the string itself, but simply copy the length of characters, and 2) the string could contain any character in the character set, with no exception for a special ending delimiter. When computers generate the information, the counting of characters ceases to be a problem, while the two advantages remain. In FORTRAN 775 the modern notation for representing strings was introduced: FORMAT(‘HELLO, WORLD’) Here the compiler had to scan the otherwise uninteresting string in order to count the characters, and the string could not contain single quotes. In situations where hardware are to interpret data, such as in CPUs and network equipment, it is common to use fixed format layouts, as illustrated by the header part of IPv4 packets6 in Figure 1. Also, here the length (in this case of the packet with payload) is given explicitly as in Standard FORTRAN, but note that the “Total Length” attribute is limited to 16 bit numbers and the address fields are limited to 32 bits. Although practical in situations with dedicated hardware, such limitations create problems over time when the length of the fields become too short and new extended protocols such as IPv6 needs to be introduced. It is thus wise to avoid fixed formats and limited lengths of size fields. 286 Mattias Nordahl and Boris Magnusson / Procedia Computer Science 56 ( 2015 ) 284 – 291 Figure 1. IPv4 header. More recent formats, geared towards human readability and easy construction, such as XML and JSON, have chosen representations in line with the FORTRAN 77 format, with explicit delimiters around strings. This notation has the drawback that all user data need to be parsed in order to understand the structure of the information. Additionally, binary information, e.g. images, which are often large by comparison (several MB) needs to be converted to readable text (e.g. through base64 encoding) before inclusion in such messages which increase their size by 1/3, adds more work for the programmer and slows down generation and parsing of messages. With PON we set out to define a format for structured data that combines the structure and compactness offered by JSON with more efficient handling for both textual and binary data. 1.2. Previous work Since JSON was popularized, several new formats have been designed that borrow from or build upon its structure, but simultaneously try to improve certain aspects where JSON falls short. The perhaps most apparent shortcoming of JSON, as suggested by the numeral binary JSON-like formats, is its lack of direct support for handling binary information. BSON11, BJSON12 and Universal Binary JSON13 are all examples of such formats. These formats share the general structuring of data with JSON, but are fully binary rather than textual, which enabled them to makes use of byte or bit manipulation to greatly decrease the number of bytes needed to represent the same data and to speed up processing. Like in Standard FORTRAN, these formats include the length of strings and other data types before their actual value, meaning that they do not need to parse the data itself, looking for an ending delimiter. 2. The PalCom Object Notation The PalCom Object Notation is also a JSON-like format, and can easily be translated to and from it. Like FORTRAN and the previously mentioned binary JSON-like formats, each value is prepended with its own length. Unlike those formats, however, PON is a fully textual format, like JSON, and can therefore be seen as mix of the two variations. Due to knowing the length of values and thus not needing to parse them, PON can directly handle binary information, while still maintaining a textual and thus readable format. 2.1. PON structure and node syntax The overall structure of PON is borrowed directly from JSON, and data is thus organized by objects and arrays, where objects are lists of key-value pairs and arrays are list of values.2 Objects and arrays are themselves also considered values, which enables nesting. In PON all values are represented as nodes that consists of three fields; the length of the value, a type identifier and the actual data value itself: <Length><Type><Data> Mattias Nordahl and Boris Magnusson / Procedia Computer Science 56 ( 2015 ) 284 – 291 287 Length is an integer stating the length of the data, the type identifier is a one byte ASCII character that determines the value type and on which the format of the data field depend. The three fields are written in sequence without spaces or delimiters, as are all nodes themselves. For instance, three consecutive string values are written: 15sMy first string16sMy second string15sMy third string 2.2. Objects, arrays and keys Since objects and arrays are also values they too follow the node structure. An array is a list of values, and its data field is thus a list of nodes, written in sequence, and their combined length is that of the array’s length field. Similarly, objects are lists of key-value pairs. Keys are the only exception to the node structure, instead being more closely represented as they are in JSON, i.e.
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